{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "5LZkDfavuZfH"
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "import numpy as np\n",
    "import yfinance as yf\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.layers import LSTM, Dense, Dropout, Conv1D, MaxPooling1D, Flatten\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score  # 导入评估指标函数\n",
    "import matplotlib.pyplot as plt  # 导入数据可视化库 Matplotlib\n",
    "import yfinance as yf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import pickle\n",
    "from tqdm.notebook import tnrange\n",
    "import seaborn as sns\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3Bh1CjNHuefO"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "v2DrT_Fluhmo"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import pickle\n",
    "from tqdm.notebook import tnrange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9tgxxH2ZunW4",
    "outputId": "e600c7ea-d8db-40e7-e9aa-5ad4ccd574ea"
   },
   "outputs": [],
   "source": [
    "# scale train and test data to new feature range[0, 1]将训练和测试数据缩放到新的特征范围[0，1]\n",
    "def scale(train, test):\n",
    "\t# fit scaler\n",
    "\tscaler = MinMaxScaler(feature_range=(0, 1))\n",
    "\tscaler = scaler.fit(train)\n",
    "\t# transform train\n",
    "\ttrain = train.reshape(train.shape[0], train.shape[1])\n",
    "\ttrain_scaled = scaler.transform(train)\n",
    "\t# transform test\n",
    "\ttest = test.reshape(test.shape[0], test.shape[1])\n",
    "\ttest_scaled = scaler.transform(test)\n",
    "\treturn scaler, train_scaled, test_scaled\n",
    "\n",
    "# compute wMAPE weighted absolute percentage error计算wMAPE加权绝对百分比误差\n",
    "def wMAPE(actual, predicted): \n",
    "    result_nom = 0\n",
    "    result_deno = 0\n",
    "    for i in range(len(actual)):\n",
    "        result_nom +=  abs(actual[i] - predicted[i])\n",
    "        result_deno +=  abs(actual[i]) \n",
    "    result = result_nom/result_deno\n",
    "    return result *100\n",
    "\n",
    "def scaled(X, y):\n",
    "    max_train = np.max(X, axis=0) #标准化\n",
    "    min_train = np.min(X, axis=0)\n",
    "    X = 0.0 + (X - min_train) / (max_train - min_train)\n",
    "    max_targets = np.max(y, axis=0)\n",
    "    min_targets = np.min(y, axis=0)\n",
    "    y = 0.0 + (y - min_targets) / (max_targets - min_targets)\n",
    "    return X, y, max_train, min_train, max_targets, min_targets\n",
    "\n",
    "def inscaled(ypred):\n",
    "    y_pred = ypred * (max_targets - min_targets) + min_targets\n",
    "    return y_pred\n",
    "\n",
    "# compute wMAPE weighted absolute percentage error计算wMAPE加权绝对百分比误差\n",
    "def wMAPE(actual, predicted): \n",
    "    result_nom = 0\n",
    "    result_deno = 0\n",
    "    for i in range(len(actual)):\n",
    "        result_nom +=  abs(actual[i] - predicted[i])\n",
    "        result_deno +=  abs(actual[i]) \n",
    "    result = result_nom/result_deno\n",
    "    return result *100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IX7fxTzQviVT",
    "outputId": "e0a865de-e3a3-42ef-94ec-2ede85635bd2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       radiation  air temperature  air pressure  humidity  actual power/MW\n",
      "0            0.0            -14.1       908.672    81.997              0.0\n",
      "1            0.0            -14.6       908.672    82.597              0.0\n",
      "2            0.0            -14.4       908.672    82.897              0.0\n",
      "3            0.0            -14.8       908.672    82.997              0.0\n",
      "4            0.0            -14.8       909.172    82.897              0.0\n",
      "...          ...              ...           ...       ...              ...\n",
      "35021        0.0             -4.8       899.600    54.900              0.0\n",
      "35022        0.0             -5.9       900.100    55.800              0.0\n",
      "35023        0.0             -6.5       900.100    57.900              0.0\n",
      "35024        0.0             -7.4       900.100    59.400              0.0\n",
      "35025        0.0             -8.0       900.100    62.700              0.0\n",
      "\n",
      "[35026 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "df = pd.read_csv('PVDATA.csv')\n",
    "# 删除时间列\n",
    "df = df.drop(columns=['TIME'])\n",
    "# 目标列\n",
    "target_column = 'actual power/MW'\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "id": "VkRMpjH3vsxV",
    "outputId": "0cefe31f-2a9b-4ad4-9a0e-edf704532cfa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0.    -14.1   908.672  81.997]\n",
      " [  0.    -14.6   908.672  82.597]\n",
      " [  0.    -14.4   908.672  82.897]\n",
      " ...\n",
      " [  0.     -6.5   900.1    57.9  ]\n",
      " [  0.     -7.4   900.1    59.4  ]\n",
      " [  0.     -8.    900.1    62.7  ]]\n"
     ]
    }
   ],
   "source": [
    "X = df.drop(columns=target_column).values  # 从 DataFrame `df` 中去除目标列 `target_column`，并将剩余的列转换为数组赋值给变量 `X`\n",
    "\n",
    "y = df[target_column].values  # 从 DataFrame `df` 中选择目标列 `target_column`，并将其转换为数组赋值给变量 `y`\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "AELPEuNEvvIu",
    "outputId": "4580e7bf-d929-4a5b-f844-83a7dd037c09"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.          0.70982043 -0.1581825  -0.25278188  0.63564631]\n",
      " [ 0.70982043  1.         -0.59307447 -0.14555079  0.57492931]\n",
      " [-0.1581825  -0.59307447  1.         -0.0128098  -0.2217157 ]\n",
      " [-0.25278188 -0.14555079 -0.0128098   1.         -0.29023335]\n",
      " [ 0.63564631  0.57492931 -0.2217157  -0.29023335  1.        ]]\n"
     ]
    }
   ],
   "source": [
    "X, y, max_train, min_train, max_targets, min_targets = scaled(X, y)#标准化\n",
    "# 计算皮尔逊相关系数矩阵\n",
    "corr = np.corrcoef(X,y,rowvar=False)\n",
    "print(corr)\n",
    "# 计算每个特征的相关性权重\n",
    "weights = np.abs(corr[-1, :-1])\n",
    "# 对数据进行加权\n",
    "X = np.multiply(X, weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 517
    },
    "id": "9FJ0l2uyv-c_",
    "outputId": "a1529b5a-fa6a-43e1-aceb-6071052092be"
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 3000x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_names = [ 'radiation','air temperature', 'air pressure','humidity','actual power/MW']\n",
    "plt.figure(figsize=(30,20))\n",
    "sns.set(font_scale=1.2)\n",
    "sns.heatmap(corr, annot=True, cmap='coolwarm', fmt='.2f', annot_kws={\"size\": 12},\n",
    "            xticklabels=feature_names, yticklabels=feature_names,)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 517
    },
    "id": "GMwtGRGuwAYo",
    "outputId": "6e59772b-496a-4fea-a2d8-4217b1861052"
   },
   "outputs": [],
   "source": [
    "'''定义一个函数用于创建数据集，输入参数包括:\n",
    "dataset: 数据集\n",
    "look_back: 回溯长度，即用多少个时间步作为输入预测下一个时间步'''\n",
    "def creat_dataset(dataset, look_back):\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(dataset) - look_back - 1):# 循环遍历数据集\n",
    "        a = dataset[i: (i + look_back)]# 提取回溯长度的数据作为输入\n",
    "        dataX.append(a)# 将该输入数据添加到输入数据集\n",
    "        dataY.append(dataset[i + look_back])# 将下一个时间步的数据添加到输出数据集\n",
    "    return np.array(dataX), np.array(dataY)# 将输入输出数据集转换为numpy数组并返回"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "AF2Q7kW8wGKz"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train X shape: (28020,)\n"
     ]
    }
   ],
   "source": [
    "dataset = y\n",
    "train_size = int(len(dataset) * 0.8)  # 计算训练集大小，将数据集长度的 80% 转换为整数\n",
    "test_size = len(dataset) - train_size  # 计算测试集大小，将剩余的数据作为测试集\n",
    "train, test = dataset[0: train_size], dataset[train_size: len(dataset)]  # 将数据集划分为训练集和测试集，使用切片操作实现\n",
    "print(\"Train X shape:\", train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "tG_r8zTO0LiE"
   },
   "outputs": [],
   "source": [
    "look_back = 1\n",
    "train_X, train_Y = creat_dataset(train, look_back)  # 根据训练集数据和滑动窗口大小创建训练集的输入序列和输出值\n",
    "test_X, test_Y = creat_dataset(test, look_back)  # 根据测试集数据和滑动窗口大小创建测试集的输入序列和输出值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "ads7b1Av0KD5"
   },
   "outputs": [],
   "source": [
    "# 调用GPU加速\n",
    "gpus = tf.config.experimental.list_physical_devices(device_type='GPU')\n",
    "for gpu in gpus:\n",
    "    tf.config.experimental.set_memory_growth(gpu, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a8cA0UEBwo3N",
    "outputId": "175cefe4-156a-402b-93bc-1d6d98c116fe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train X shape: (28018, 1)\n",
      "Train Y shape: (28018,)\n",
      "Test X shape: (7004, 1)\n",
      "Test Y shape: (7004,)\n"
     ]
    }
   ],
   "source": [
    "# Ensure the sizes of training and testing sets\n",
    "print(\"Train X shape:\", train_X.shape)\n",
    "print(\"Train Y shape:\", train_Y.shape)\n",
    "print(\"Test X shape:\", test_X.shape)\n",
    "print(\"Test Y shape:\", test_Y.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# input_layer = Input(shape=(train_X.shape[1], 1))\n",
    "# print(input_layer.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "_mq48nepzY_I"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import ModelCheckpoint , ReduceLROnPlateau\n",
    "\n",
    "save_best = ModelCheckpoint(\"best_weights.h5\", monitor='val_loss', save_best_only=True, save_weights_only=True)\n",
    "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.25,patience=4, min_lr=0.00001,verbose = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "TBrMgLrIwsGv"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "876/876 [==============================] - 1s 846us/step - loss: 0.0088 - val_loss: 0.0028 - lr: 0.0010\n",
      "Epoch 2/50\n",
      "876/876 [==============================] - 1s 780us/step - loss: 0.0049 - val_loss: 0.0029 - lr: 0.0010\n",
      "Epoch 3/50\n",
      "876/876 [==============================] - 1s 764us/step - loss: 0.0049 - val_loss: 0.0028 - lr: 0.0010\n",
      "Epoch 4/50\n",
      "876/876 [==============================] - 1s 769us/step - loss: 0.0049 - val_loss: 0.0029 - lr: 0.0010\n",
      "Epoch 5/50\n",
      "804/876 [==========================>...] - ETA: 0s - loss: 0.0048\n",
      "Epoch 5: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "876/876 [==============================] - 1s 770us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 0.0010\n",
      "Epoch 6/50\n",
      "876/876 [==============================] - 1s 769us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 2.5000e-04\n",
      "Epoch 7/50\n",
      "876/876 [==============================] - 1s 774us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 2.5000e-04\n",
      "Epoch 8/50\n",
      "876/876 [==============================] - 1s 762us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 2.5000e-04\n",
      "Epoch 9/50\n",
      "808/876 [==========================>...] - ETA: 0s - loss: 0.0048\n",
      "Epoch 9: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
      "876/876 [==============================] - 1s 775us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 2.5000e-04\n",
      "Epoch 10/50\n",
      "876/876 [==============================] - 1s 780us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 6.2500e-05\n",
      "Epoch 11/50\n",
      "876/876 [==============================] - 1s 778us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 6.2500e-05\n",
      "Epoch 12/50\n",
      "876/876 [==============================] - 1s 786us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 6.2500e-05\n",
      "Epoch 13/50\n",
      "799/876 [==========================>...] - ETA: 0s - loss: 0.0047\n",
      "Epoch 13: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05.\n",
      "876/876 [==============================] - 1s 775us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 6.2500e-05\n",
      "Epoch 14/50\n",
      "876/876 [==============================] - 1s 771us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.5625e-05\n",
      "Epoch 15/50\n",
      "876/876 [==============================] - 1s 772us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.5625e-05\n",
      "Epoch 16/50\n",
      "876/876 [==============================] - 1s 780us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.5625e-05\n",
      "Epoch 17/50\n",
      "808/876 [==========================>...] - ETA: 0s - loss: 0.0048\n",
      "Epoch 17: ReduceLROnPlateau reducing learning rate to 1e-05.\n",
      "876/876 [==============================] - 1s 770us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.5625e-05\n",
      "Epoch 18/50\n",
      "876/876 [==============================] - 1s 770us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 19/50\n",
      "876/876 [==============================] - 1s 782us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 20/50\n",
      "876/876 [==============================] - 1s 775us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 21/50\n",
      "876/876 [==============================] - 1s 772us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 22/50\n",
      "876/876 [==============================] - 1s 774us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 23/50\n",
      "876/876 [==============================] - 1s 764us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 24/50\n",
      "876/876 [==============================] - 1s 770us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 25/50\n",
      "876/876 [==============================] - 1s 774us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 26/50\n",
      "876/876 [==============================] - 1s 769us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 27/50\n",
      "876/876 [==============================] - 1s 768us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 28/50\n",
      "876/876 [==============================] - 1s 760us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 29/50\n",
      "876/876 [==============================] - 1s 766us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 30/50\n",
      "876/876 [==============================] - 1s 768us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 31/50\n",
      "876/876 [==============================] - 1s 764us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 32/50\n",
      "876/876 [==============================] - 1s 767us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 33/50\n",
      "876/876 [==============================] - 1s 752us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 34/50\n",
      "876/876 [==============================] - 1s 762us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 35/50\n",
      "876/876 [==============================] - 1s 767us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 36/50\n",
      "876/876 [==============================] - 1s 762us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 37/50\n",
      "876/876 [==============================] - 1s 773us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 38/50\n",
      "876/876 [==============================] - 1s 764us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 39/50\n",
      "876/876 [==============================] - 1s 768us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 40/50\n",
      "876/876 [==============================] - 1s 767us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 41/50\n",
      "876/876 [==============================] - 1s 765us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 42/50\n",
      "876/876 [==============================] - 1s 769us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 43/50\n",
      "876/876 [==============================] - 1s 761us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 44/50\n",
      "876/876 [==============================] - 1s 768us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 45/50\n",
      "876/876 [==============================] - 1s 762us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 46/50\n",
      "876/876 [==============================] - 1s 775us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 47/50\n",
      "876/876 [==============================] - 1s 765us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 48/50\n",
      "876/876 [==============================] - 1s 765us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 49/50\n",
      "876/876 [==============================] - 1s 763us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 50/50\n",
      "876/876 [==============================] - 1s 766us/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x17c24904400>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.layers import Concatenate, Input\n",
    "from keras.models import Model\n",
    "# Define the input layer shape for both LSTM and CNN\n",
    "input_layer = Input(shape=(train_X.shape[1], 1))\n",
    "\n",
    "# Build the CNN model\n",
    "cnn_model = Sequential()\n",
    "cnn_model.add(Conv1D(filters=32, kernel_size=1, activation='relu', input_shape=(train_X.shape[1], 1)))\n",
    "cnn_model.add(MaxPooling1D(pool_size=1))\n",
    "cnn_model.add(Flatten())\n",
    "cnn_model.add(Dense(units=1, activation='relu'))\n",
    "cnn_model.compile(optimizer='adam', loss='mse')\n",
    "# Train the LSTM model\n",
    "cnn_model.fit(train_X, train_Y, epochs=50, batch_size=32,validation_data=(test_X , test_Y),\n",
    "            callbacks=[reduce_lr , save_best])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "876/876 [==============================] - 0s 504us/step\n",
      "219/219 [==============================] - 0s 508us/step\n"
     ]
    }
   ],
   "source": [
    "# 对训练集进行预测\n",
    "ypred_train = cnn_model.predict(train_X)\n",
    "# 将训练集的预测结果转换为原始数据的范围\n",
    "ypred_train = inscaled(ypred_train)\n",
    "# 将训练集的真实标签转换为原始数据的范围\n",
    "y_train = inscaled(train_Y)\n",
    "\n",
    "# 对测试集进行预测\n",
    "ypred_test = cnn_model.predict(test_X)\n",
    "# 将测试集的预测结果转换为原始数据的范围\n",
    "ypred_test = inscaled(ypred_test)\n",
    "# 将测试集的真实标签转换为原始数据的范围\n",
    "y_test = inscaled(test_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wvkFS4KjwxIY",
    "outputId": "f6839368-9d52-419c-f04c-ce18572bcb8f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7004, 1)\n",
      "(7004,)\n"
     ]
    }
   ],
   "source": [
    "print(ypred_test.shape)\n",
    "print(y_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = y_test[0:len(ypred_test)-1]\n",
    "ypred_test = ypred_test[1:len(ypred_test),0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8OGCdy0MwzJm",
    "outputId": "77561502-eb1e-4fe3-edf2-3065c5185729"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. ... 0. 0. 0.]\n",
      "RMSE 7.540 \n",
      "MAE 3.624 \n",
      "R2 -0.301 \n",
      "wMAPE 100.000 \n"
     ]
    }
   ],
   "source": [
    "print(y_test)\n",
    "# 计算测试集的均方根误差（RMSE）\n",
    "testScore = math.sqrt(mean_squared_error(y_test, ypred_test))\n",
    "print('RMSE %.3f ' %(testScore))\n",
    "# 计算测试集的平均绝对误差（MAE）\n",
    "testScore = mean_absolute_error(y_test, ypred_test)\n",
    "print('MAE %.3f ' %(testScore))\n",
    "# 计算测试集的R平方值（R2）\n",
    "testScore = r2_score(y_test, ypred_test)\n",
    "print('R2 %.3f ' %(testScore))\n",
    "testScore = wMAPE(y_test, ypred_test)\n",
    "print('wMAPE %.3f ' %(testScore))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "4Qke2Du1w4o_"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出测试集预测结果与真实值的对比折线图\n",
    "plt.figure(figsize=(12, 3))\n",
    "# 创建一个图形窗口，设置图形窗口的大小为宽度 12，高度 3\n",
    "plt.plot(y_test[1300:1500], label='True', color='r', linewidth=1) # 绘制 y_test 数据的折线图，并设置标签为 'Actual'\n",
    "plt.plot(ypred_test[1300:1500], label='Predicted', color='b' , linewidth=1, linestyle=\"--\") # 绘制 y_pred 数据的折线图，并设置标签为 'Predicted'\n",
    "plt.title('CNN Prediction', size=10)  # 设置图形的标题为'DBO-LSTM Prediction'，字体大小为10\n",
    "plt.ylabel('PV/MW', fontsize=10)  # 设置y轴标签为'PV（kWh）'，字体大小为10\n",
    "plt.xlabel('Sampling points', fontsize=10)  # 设置x轴标签为'Sampling points'，字体大小为10\n",
    "plt.legend()\n",
    "plt.savefig(\"CNN.png\")\n",
    "# 显示图例\n",
    "plt.show()\n",
    "\n",
    "# 显示图形窗口"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pACCIDAMw7a8",
    "outputId": "d4c67dce-4cd5-4f5d-90c5-ef4806e436a6"
   },
   "outputs": [],
   "source": [
    "import xlrd\n",
    "import xlwt  #对xls文件进行改写\n",
    "from xlutils.copy import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vtJodmNww988",
    "outputId": "d83f12dc-a917-48f3-ec1a-68870692cafd"
   },
   "outputs": [],
   "source": [
    "# 新建表格\n",
    "def excel_create(path, sheet_name):\n",
    "    workbook = xlwt.Workbook(encoding = 'utf-8',style_compression = 0)  #相当于创建了一个EXCEL文件，style_compression:表示是否压缩，不常用)\n",
    "    workbook.add_sheet(sheet_name)  # 在工作簿中新建一个表格\n",
    "    workbook.save(path)  # 保存工作簿\n",
    "    \n",
    "# 写入表头\n",
    "def excel_write_title(path, title):\n",
    "    workbook = xlrd.open_workbook(path)  # 打开工作簿\n",
    "    new_workbook = copy(workbook)  # 拷贝原工作簿\n",
    "    new_worksheet = new_workbook.get_sheet(0)  # 获取转化后工作簿中的第一个表格，即index=0\n",
    "    for j in range(0, len(title)):\n",
    "        new_worksheet.write(0, j, str(title[j]))  # 在表格中第一行（row=0)写入标题\n",
    "    new_workbook.save(path)  # 保存工作簿\n",
    " \n",
    "# 向表格按列写入一维数组（列表）\n",
    "def excel_write_array(path, value, column):\n",
    "    workbook = xlrd.open_workbook(path)  # 打开工作簿\n",
    "    new_workbook = copy(workbook)  # 拷贝原工作簿\n",
    "    new_worksheet = new_workbook.get_sheet(0)  # 获取转化后工作簿中的第一个表格\n",
    "    for i in range(0, len(value)):\n",
    "        new_worksheet.write(i+1, column, float(value[i]))# 从第二行开始(row=i+1)，按对应的列(column)向表格中写入数据\n",
    "    new_workbook.save(path)  # 保存工作簿\n",
    " \n",
    "# 向表格写入二维数组（列表）\n",
    "def excel_write_array2(path, value):\n",
    "    workbook = xlrd.open_workbook(path)  # 打开工作簿\n",
    "    new_workbook = copy(workbook)  # 拷贝原工作簿\n",
    "    new_worksheet = new_workbook.get_sheet(0)  # 获取转化后工作簿中的第一个表格\n",
    "    for i in range(0, len(value)):\n",
    "        for j in range(len(value[0])):\n",
    "            new_worksheet.write(i+1, j, float(value[i][j]))# 从第二行开始(row=i+1)，按对应的行和列向表格中写入数据\n",
    "    new_workbook.save(path)  # 保存工作簿"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "id": "8T-XP7dnzkQr"
   },
   "outputs": [],
   "source": [
    "path       = r'step3-CNN.xls' \n",
    "sheet_name = 'NOx'\n",
    "title      = ['CNN','y_test'] #表头\n",
    " \n",
    "excel_create(path, sheet_name) #新建表格\n",
    "excel_write_title(path, title) #写入表头\n",
    "#写入需要的一维数组(lat,lon,nox是之前已有的一维数组）\n",
    "excel_write_array(path, ypred_test,0)\n",
    "excel_write_array(path, y_test,1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "provenance": []
  },
  "gpuClass": "standard",
  "kernelspec": {
   "display_name": "qqqq",
   "language": "python",
   "name": "qqqq"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
